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https://github.com/Hestia-Homes/Model.git
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Merge pull request #358 from Hestia-Homes/remote-assessment
Remote assessment
This commit is contained in:
KhalimCK
GitHub
commit
27737d82fd
12 changed files with 510 additions and 95 deletions
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@ -75,6 +75,7 @@ class Property:
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postcode,
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address,
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epc_record,
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property_valuation=None,
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already_installed=None,
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non_invasive_recommendations=None,
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measures=None,
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@ -111,6 +112,8 @@ class Property:
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else:
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self.measures = ast.literal_eval(measures) if measures else None
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self.valuation = property_valuation
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self.uprn = epc_record.get("uprn")
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self.uprn_source = self.data.get("uprn-source")
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@ -535,7 +538,8 @@ class Property:
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"loft_insulation", "room_roof_insulation", "flat_roof_insulation",
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"solid_floor_insulation", "suspended_floor_insulation",
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"windows_glazing", "solar_pv", "heating", "hot_water_tank_insulation",
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"heating_control", "secondary_heating", "cylinder_thermostat", "mixed_glazing"
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"heating_control", "secondary_heating", "cylinder_thermostat", "mixed_glazing",
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"extension_cavity_wall_insulation",
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]:
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raise NotImplementedError(
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"Implement me, given type %s" % recommendation["type"]
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@ -272,23 +272,10 @@ class GoogleSolarApi:
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roi_summary = []
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for segment in roof_segment_summaries:
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if segment["panelsCount"] < min_panels:
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continue
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wattage = segment["panelsCount"] * self.insights_data["solarPotential"]["panelCapacityWatts"]
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generated_dc_energy = segment["yearlyEnergyDcKwh"]
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ratio = generated_dc_energy / wattage
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if cost_instance is None:
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cost = MCS_SOLAR_PV_COST_DATA["average_cost_per_kwh"] * (wattage / 1000)
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else:
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cost = cost_instance.solar_pv(
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n_panels=segment["panelsCount"],
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has_battery=False,
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n_floors=property_instance.number_of_floors,
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)["total"]
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roi_summary.append(
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{
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"segmentIndex": segment["segmentIndex"],
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@ -296,7 +283,6 @@ class GoogleSolarApi:
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"generated_dc_energy": generated_dc_energy,
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"ratio": ratio,
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"n_panels": segment["panelsCount"],
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"cost": cost,
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"panneled_roof_area": self.panel_area * int(segment["panelsCount"])
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}
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)
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@ -305,10 +291,21 @@ class GoogleSolarApi:
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if roi_summary.empty:
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continue
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if roi_summary["n_panels"].sum() < min_panels:
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continue
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if cost_instance is None:
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total_cost = MCS_SOLAR_PV_COST_DATA["average_cost_per_kwh"] * (wattage / 1000)
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else:
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total_cost = cost_instance.solar_pv(
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n_panels=roi_summary["n_panels"].sum(),
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has_battery=False,
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n_floors=property_instance.number_of_floors,
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)["total"]
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weighted_ratio = np.average(
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roi_summary["ratio"].values, weights=roi_summary["generated_dc_energy"].values
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)
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total_cost = roi_summary["cost"].sum()
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yearly_dc_energy = roi_summary["generated_dc_energy"].sum()
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panel_performance.append(
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@ -4,8 +4,10 @@ PESSIMISTIC_ASHP_EFFICIENCY = 200
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AVERAGE_ASHP_EFFICIENCY = 250
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# Conservative estimate of the proportion of electricity that will be consumed, whereas the rest will
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# be exported
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# be exported. These are averages based on Google research. E.g
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# https://www.nea.org.uk/who-we-are/innovation-technical-evaluation/solarpv/solarpv-batteries
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SOLAR_CONSUMPTION_PROPORTION = 0.5
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SOLAR_CONSUMPTION_WITH_BATTERY_PROPORTION = 0.7
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# Typically, each solar panel takes up around 3.4 m2 of roof space under RdSAP. This was been verified in Elmhurst
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RDSAP_AREA_PER_PANEL = 3.4
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@ -289,7 +289,7 @@ def create_epc_records(epc_searcher: SearchEpc, energy_assessment: dict):
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}, energy_assessment_is_newer
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def get_on_site_data(body: PlanTriggerRequest):
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def get_request_property_data(body: PlanTriggerRequest):
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"""
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This function will read in the on-site data from the S3 bucket
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:param body: The request body
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@ -311,10 +311,18 @@ def get_on_site_data(body: PlanTriggerRequest):
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bucket_name=get_settings().PLAN_TRIGGER_BUCKET, filepath=body.non_invasive_recommendations_file_path
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)
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return patches, already_installed, non_invasive_recommendations
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valuation_data = []
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if body.valuation_file_path:
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valuation_data = read_csv_from_s3(
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bucket_name=get_settings().PLAN_TRIGGER_BUCKET, filepath=body.valuation_file_path
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)
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return patches, already_installed, non_invasive_recommendations, valuation_data
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def extract_property_on_site_recommendations(config, patches, already_installed, non_invasive_recommendations, uprn):
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def extract_property_request_data(
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config, patches, already_installed, non_invasive_recommendations, valuation_data, uprn
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):
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patch_has_uprn = "uprn" in patches[0] if patches else True
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if patch_has_uprn:
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patch = next((
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@ -360,7 +368,12 @@ def extract_property_on_site_recommendations(config, patches, already_installed,
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property_non_invasive_recommendations["recommendations"] = str(transformed)
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return patch, property_already_installed, property_non_invasive_recommendations
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property_valution = next((
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float(x["value"]) for x in valuation_data if
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(str(x["uprn"]) == str(uprn))
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), None)
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return patch, property_already_installed, property_non_invasive_recommendations, property_valution
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router = APIRouter(
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@ -384,7 +397,7 @@ async def trigger_plan(body: PlanTriggerRequest):
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logger.info("Getting the inputs")
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plan_input = read_csv_from_s3(bucket_name=get_settings().PLAN_TRIGGER_BUCKET, filepath=body.trigger_file_path)
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# If we have patches or overrides, we should read them in here
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patches, already_installed, non_invasive_recommendations = get_on_site_data(body)
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patches, already_installed, non_invasive_recommendations, valuation_data = get_request_property_data(body)
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cleaning_data = read_dataframe_from_s3_parquet(
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bucket_name=get_settings().DATA_BUCKET, file_key="sap_change_model/cleaning_dataset.parquet",
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@ -412,8 +425,6 @@ async def trigger_plan(body: PlanTriggerRequest):
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# We check for an energy assessment we have performed on this property:
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energy_assessment = get_latest_assessment_by_uprn(session, uprn if uprn is not None else epc_searcher.uprn)
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if not energy_assessment["epc"]:
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continue
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# Create a record in db
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property_id, is_new = create_property(
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session, body.portfolio_id, epc_searcher.address_clean, epc_searcher.postcode_clean,
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@ -440,9 +451,14 @@ async def trigger_plan(body: PlanTriggerRequest):
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epc_searcher, energy_assessment
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)
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patch, property_already_installed, property_non_invasive_recommendations = (
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extract_property_on_site_recommendations(
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config, patches, already_installed, non_invasive_recommendations, uprn
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patch, property_already_installed, property_non_invasive_recommendations, property_valuation = (
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extract_property_request_data(
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config=config,
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patches=patches,
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already_installed=already_installed,
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non_invasive_recommendations=non_invasive_recommendations,
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valuation_data=valuation_data,
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uprn=epc_searcher.uprn,
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)
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)
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@ -462,6 +478,7 @@ async def trigger_plan(body: PlanTriggerRequest):
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postcode=epc_searcher.postcode_clean,
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epc_record=prepared_epc,
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already_installed=property_already_installed,
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property_valuation=property_valuation,
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non_invasive_recommendations=property_non_invasive_recommendations,
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energy_assessment=energy_assessment,
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**Property.extract_kwargs(config), # TODO: Depraecate this
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@ -52,6 +52,9 @@ NON_INVASIVE_SPECIFIC_MEASURES = [
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"draught_proofing",
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"mixed_glazing", # This covers partial double glazing and secondary glazing
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"cavity_extract_and_refill",
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# Indicates that there is one (need to handle the case where there are multiple)
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# extension that requires cavity wall insulation
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"extension_cavity_wall_insulation",
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]
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# This allows us to extend high level categories for measures such as "wall_insulation" to the specific measures
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@ -78,6 +81,7 @@ class PlanTriggerRequest(BaseModel):
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already_installed_file_path: Optional[str] = None
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patches_file_path: Optional[str] = None
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non_invasive_recommendations_file_path: Optional[str] = None
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valuation_file_path: Optional[str] = None
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exclusions: Optional[conlist(str, min_items=1)] = None
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inclusions: Optional[conlist(str, min_items=1)] = None
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@ -1,4 +1,5 @@
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import numpy as np
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from scipy.constants import value
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class PropertyValuation:
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@ -203,9 +204,12 @@ class PropertyValuation:
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@classmethod
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def estimate(cls, property_instance, target_epc):
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value = cls.UPRN_VALUE_LOOKUP.get(property_instance.uprn)
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current_value = (
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property_instance.valuation if property_instance.valuation else
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cls.UPRN_VALUE_LOOKUP.get(property_instance.uprn)
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)
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if not value:
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if not current_value:
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return {
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"current_value": 0,
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"lower_bound_increased_value": 0,
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@ -235,12 +239,13 @@ class PropertyValuation:
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max_increase = max(all_increases)
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min_increase = min(all_increases)
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avg_increase = np.mean(all_increases)
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return {
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"current_value": value,
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"lower_bound_increased_value": value * (1 + min_increase),
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"upper_bound_increased_value": value * (1 + max_increase),
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"average_increased_value": value * (1 + avg_increase),
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"average_increase": value * (1 + avg_increase) - value
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"current_value": current_value,
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"lower_bound_increased_value": current_value * (1 + min_increase),
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"upper_bound_increased_value": current_value * (1 + max_increase),
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"average_increased_value": current_value * (1 + avg_increase),
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"average_increase": current_value * (1 + avg_increase) - current_value
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}
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@ -417,9 +417,14 @@ def slides():
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# Show more characters in a column
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pd.set_option('display.max_colwidth', None)
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# preparing of this data for the following 2 needs:
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# 1) dataset to share with Nextgen heating
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# 2) Breakdown of results by property type
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def lewes_outputs():
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"""
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preparing of this data for the following 2 needs:
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1) dataset to share with Nextgen heating
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2) Breakdown of results by property type
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:return:
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"""
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# get the asset list
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asset_list = read_csv_from_s3(bucket_name="retrofit-plan-inputs-dev", filepath="8/90/pilot.csv")
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@ -431,6 +436,14 @@ def slides():
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)
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non_intrusive_recommendations = pd.DataFrame(non_intrusive_recommendations)
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# Right now this is the second version of the nehaven portfolio
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portfolio_id = 90
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# Look at one scenario at a time, otherwise this is agony
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scenario_ids = [47, 48, 49, 50, 51]
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properties_data, plans_data, recommendations_data = get_data(portfolio_id, scenario_ids)
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properties_df = pd.DataFrame(properties_data)
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recommendations_df = pd.DataFrame(recommendations_data)
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# Unnest this
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import ast
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survey_recs = []
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@ -502,27 +515,73 @@ def slides():
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# We now pull out the recommendations impact by property type and sub type
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# Exclude sealing open fireplaces
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recommendations_df = recommendations_df[recommendations_df["type"] != "sealing_open_fireplace"]
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# We update the type column so that if type == heating, and the description contains "air source heat pump",
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# the type is "air_source_heat_pump", else if the description contains "high heat retention storage heaters", else
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# if the description contains "condensing boiler, the type is updated to "boiler_upgrade"
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recommendations_df["type"] = np.where(
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recommendations_df["type"] == "heating",
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np.where(
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recommendations_df["description"].str.contains("air source heat pump"),
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"Air Source Heat Pump",
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np.where(
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recommendations_df["description"].str.contains("high heat retention"),
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"High Heat Retention Storage",
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np.where(
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recommendations_df["description"].str.contains("condensing boiler"),
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"Boiler Upgrade",
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recommendations_df["type"]
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)
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)
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),
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recommendations_df["type"]
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)
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recommendation_types = recommendations_df["type"].unique().tolist()
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rename_dict = {
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'hot_water_tank_insulation': 'Hot Water Tank Insulation',
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'windows_glazing': 'Windows Glazing',
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'secondary_heating': 'Secondary Heating',
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'cavity_wall_insulation': 'Cavity Wall Insulation',
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'flat_roof_insulation': 'Flat Roof Insulation',
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'mechanical_ventilation': 'Mechanical Ventilation',
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'loft_insulation': 'Loft Insulation',
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'cylinder_thermostat': 'Cylinder Thermostat',
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'room_roof_insulation': 'Room Roof Insulation',
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'low_energy_lighting': 'Low Energy Lighting',
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'external_wall_insulation': 'External Wall Insulation',
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'solar_pv': 'Solar PV',
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'heating_control': 'Heating Control',
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'solid_floor_insulation': 'Solid Floor Insulation',
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'suspended_floor_insulation': 'Suspended Floor Insulation',
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'internal_wall_insulation': 'Internal Wall Insulation'
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}
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property_scenario_impact = []
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for scenario_id in scenario_ids:
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for scenario_id in tqdm(scenario_ids):
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# Get the recommendations for the scenario, default
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scenario_recommendations = recommendations_df[
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(recommendations_df["Scenario ID"] == scenario_id) &
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(recommendations_df["default"] == True)
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].copy()
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scenario_recommendations['ligting_kwh'] = scenario_recommendations.apply(
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scenario_recommendations['Estimated Lighting kWh Savings'] = scenario_recommendations.apply(
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lambda x: x['kwh_savings'] if x['type'] == 'low_energy_lighting' else 0,
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axis=1)
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scenario_recommendations['solar_kwh'] = scenario_recommendations.apply(
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scenario_recommendations['Estimated Solar kWh Savings'] = scenario_recommendations.apply(
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lambda x: x['kwh_savings'] if x['type'] == 'solar_pv' else 0, axis=1)
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# Set 'Estimated Kwh Savings' to zero where specific kwh columns are used
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scenario_recommendations['Estimated Kwh Savings'] = scenario_recommendations.apply(
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scenario_recommendations['Estimated Heating Demand kWh Savings'] = scenario_recommendations.apply(
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lambda x: 0 if x['type'] in ['low_energy_lighting', 'solar_pv'] else x[
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'kwh_savings'], axis=1)
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scenario_grouped_data = scenario_recommendations.groupby(['property_id']).agg({
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'Estimated Kwh Savings': 'sum',
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'Estimated Heating Demand kWh Savings': 'sum',
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'Estimated Lighting kWh Savings': 'sum',
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'Estimated Solar kWh Savings': 'sum',
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"estimated_cost": "sum"
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}).reset_index()
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|
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@ -531,18 +590,52 @@ def slides():
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].merge(
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scenario_grouped_data, on=["property_id"], how="left"
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)
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comparison["Estimated Kwh Savings"] = comparison["Estimated Kwh Savings"].fillna(0)
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comparison["Estimated Heating Demand kWh Savings"] = (
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comparison["Estimated Heating Demand kWh Savings"].fillna(0)
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)
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comparison["Estimated Lighting kWh Savings"] = (
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comparison["Estimated Lighting kWh Savings"].fillna(0)
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)
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comparison["Estimated Solar kWh Savings"] = (
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comparison["Estimated Solar kWh Savings"].fillna(0)
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)
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comparison["estimated_cost"] = comparison["estimated_cost"].fillna(0)
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comparison["post_scenario_heating_hotwater_kwh"] = (
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comparison["current_energy_demand_heating_hotwater"] - comparison["Estimated Kwh Savings"]
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comparison["current_energy_demand_heating_hotwater"] - comparison["Estimated Heating Demand kWh Savings"]
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)
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# For each scenario, we create a measure matrix
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measure_matrix = scenario_recommendations.pivot_table(
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index='property_id',
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columns='type',
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values='id', # Using 'id' just as a placeholder for the pivot
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aggfunc=lambda x: True, # If an ID exists for a given type, mark as True
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fill_value=False # Fill other entries as False
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).reset_index()
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non_zero_heat_demand_impact = comparison[
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(comparison["Estimated Heating Demand kWh Savings"] > 0) |
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(comparison["Estimated Lighting kWh Savings"] > 0) |
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(comparison["Estimated Solar kWh Savings"] > 0)
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]
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measure_matrix = measure_matrix[
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measure_matrix["property_id"].isin(non_zero_heat_demand_impact["property_id"].values)
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]
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measure_matrix = measure_matrix.rename(columns=rename_dict)
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comparison = comparison.merge(
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measure_matrix, on="property_id", how="left"
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)
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comparison["scenario_id"] = scenario_id
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||||
|
||||
property_scenario_impact.append(comparison)
|
||||
|
||||
property_scenario_impact = pd.concat(property_scenario_impact)
|
||||
property_scenario_impact = property_scenario_impact.drop(columns=["property_id", "Estimated Kwh Savings"])
|
||||
# property_scenario_impact = property_scenario_impact.drop(columns=["property_id", "Estimated Kwh Savings"])
|
||||
for v in list(rename_dict.values()) + ["Air Source Heat Pump", "High Heat Retention Storage", "Boiler Upgrade"]:
|
||||
# Fill NaNs with False
|
||||
property_scenario_impact[v] = property_scenario_impact[v].fillna(False)
|
||||
|
||||
# Scale
|
||||
property_scenario_impact["post_scenario_heating_hotwater_kwh_scaled"] = (
|
||||
|
|
@ -600,57 +693,119 @@ def slides():
|
|||
"post_scenario_heating_hotwater_kwh_scaled"]].empty:
|
||||
raise Exception("someting went wrong")
|
||||
|
||||
# Reorder the columns
|
||||
grouped_data = grouped_data[
|
||||
[
|
||||
'property_type',
|
||||
'property_sub_type',
|
||||
'scenario',
|
||||
'estimated_heating_hotwater_kwh',
|
||||
'post_scenario_heating_hotwater_kwh',
|
||||
'estimated_heating_hotwater_kwh_scaled',
|
||||
'post_scenario_heating_hotwater_kwh_scaled',
|
||||
'estimated_cost',
|
||||
]
|
||||
# Reorder the columns
|
||||
grouped_data = grouped_data[
|
||||
[
|
||||
'property_type',
|
||||
'property_sub_type',
|
||||
'scenario',
|
||||
'estimated_heating_hotwater_kwh',
|
||||
'post_scenario_heating_hotwater_kwh',
|
||||
'estimated_heating_hotwater_kwh_scaled',
|
||||
'post_scenario_heating_hotwater_kwh_scaled',
|
||||
'estimated_cost',
|
||||
]
|
||||
]
|
||||
|
||||
grouped_data = grouped_data.rename(
|
||||
columns={
|
||||
"property_type": "Property Type",
|
||||
"property_sub_type": "Property Sub Type",
|
||||
"scenario": "Scenario",
|
||||
"estimated_heating_hotwater_kwh": "Estimated Heating & Hot Water kwh",
|
||||
"post_scenario_heating_hotwater_kwh": "Post Scenario Heating & Hot Water kwh",
|
||||
"estimated_heating_hotwater_kwh_scaled": "Estimated Heating & Hot Water kwh (scaled)",
|
||||
"post_scenario_heating_hotwater_kwh_scaled": "Post Scenario Heating & Hot Water kwh (scaled)",
|
||||
"estimated_cost": "Estimated Cost or Retrofit",
|
||||
}
|
||||
)
|
||||
grouped_data = grouped_data.rename(
|
||||
columns={
|
||||
"property_type": "Property Type",
|
||||
"property_sub_type": "Property Sub Type",
|
||||
"scenario": "Scenario",
|
||||
"estimated_heating_hotwater_kwh": "Estimated Heating & Hot Water kwh",
|
||||
"post_scenario_heating_hotwater_kwh": "Post Scenario Heating & Hot Water kwh",
|
||||
"estimated_heating_hotwater_kwh_scaled": "Estimated Heating & Hot Water kwh (scaled)",
|
||||
"post_scenario_heating_hotwater_kwh_scaled": "Post Scenario Heating & Hot Water kwh (scaled)",
|
||||
"estimated_cost": "Estimated Cost or Retrofit",
|
||||
}
|
||||
)
|
||||
|
||||
grouped_data.to_excel(
|
||||
"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Newhaven/outputs/Scenario kWh Impact by Property "
|
||||
"Type.xlsx",
|
||||
index=False
|
||||
)
|
||||
# grouped_data.to_excel(
|
||||
# "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Newhaven/outputs/Scenario kWh Impact by Property "
|
||||
# "Type.xlsx",
|
||||
# index=False
|
||||
# )
|
||||
|
||||
property_scenario_impact = property_scenario_impact.merge(
|
||||
scenario_names, how="left", on="scenario_id"
|
||||
)
|
||||
property_scenario_impact = property_scenario_impact.merge(
|
||||
scenario_names, how="left", on="scenario_id"
|
||||
)
|
||||
|
||||
df_pivot = property_scenario_impact.pivot_table(index='uprn', columns='scenario',
|
||||
values=['post_scenario_heating_hotwater_kwh',
|
||||
'post_scenario_heating_hotwater_kwh_scaled'])
|
||||
lewes_data = next_gen_dataset.merge(
|
||||
property_scenario_impact, how="left", on="uprn"
|
||||
)
|
||||
|
||||
# Flattening multi-index columns
|
||||
df_pivot.columns = [f'{col[0]}_{col[1]}' for col in df_pivot.columns]
|
||||
lewes_data = lewes_data.sort_values(
|
||||
["postcode", "uprn", "scenario_id"], ascending=True
|
||||
)
|
||||
|
||||
# Reset the index to have a clean dataframe
|
||||
df_pivot.reset_index(inplace=True)
|
||||
# Rearrange, rename columns and drop what we don't need
|
||||
# TODO - remap the heating type
|
||||
lewes_data = lewes_data[
|
||||
[
|
||||
'uprn', 'address', 'postcode', 'property_type', 'built_form',
|
||||
# 'estimated_heating_hotwater_kwh',
|
||||
'primary_fuel_type', 'gross_floor_area', 'floor_height', 'number_of_floors', 'ashp_suitable',
|
||||
'ashp_size_kw',
|
||||
'ashp_cost', 'solar_suitable', 'solar_size_kwp', 'solar_cost',
|
||||
'scenario',
|
||||
'estimated_heating_hotwater_kwh_scaled',
|
||||
'post_scenario_heating_hotwater_kwh_scaled',
|
||||
# 'property_id', - dropped
|
||||
# 'current_energy_demand_heating_hotwater',
|
||||
'Estimated Heating Demand kWh Savings',
|
||||
'Estimated Lighting kWh Savings',
|
||||
'Estimated Solar kWh Savings',
|
||||
'estimated_cost',
|
||||
'post_scenario_heating_hotwater_kwh', 'Cavity Wall Insulation', 'Cylinder Thermostat',
|
||||
'Flat Roof Insulation',
|
||||
'Hot Water Tank Insulation', 'Loft Insulation', 'Mechanical Ventilation', 'Room Roof Insulation',
|
||||
# 'scenario_id', - dropped
|
||||
'Low Energy Lighting', 'Secondary Heating', 'Windows Glazing', 'External Wall Insulation',
|
||||
'Heating Control',
|
||||
'Solar PV',
|
||||
'Air Source Heat Pump', 'Boiler Upgrade', 'High Heat Retention Storage',
|
||||
'Internal Wall Insulation',
|
||||
'Solid Floor Insulation',
|
||||
'Suspended Floor Insulation',
|
||||
]
|
||||
].rename(
|
||||
columns={
|
||||
"primary_fuel_type": "Primary Fuel Type",
|
||||
"gross_floor_area": "Gross Floor Area",
|
||||
"floor_height": "Floor Height",
|
||||
"number_of_floors": "Number of Floors",
|
||||
"ashp_suitable": "Is an ASHP Suitable?",
|
||||
"ashp_size_kw": "ASHP Size (kW)",
|
||||
"ashp_cost": "ASHP Cost",
|
||||
"solar_suitable": "Is Solar PV Suitable?",
|
||||
"solar_size_kwp": "Solar PV Size (kWp)",
|
||||
"solar_cost": "Solar PV Cost",
|
||||
# "estimated_heating_hotwater_kwh": "Estimated Heating & Hot Water kwh",
|
||||
"estimated_heating_hotwater_kwh_scaled": "Estimated Heating & Hot Water kwh",
|
||||
"post_scenario_heating_hotwater_kwh_scaled": "Post Scenario Heating & Hot Water kwh",
|
||||
"estimated_cost": "Estimated Cost of Scenario"
|
||||
}
|
||||
)
|
||||
|
||||
next_gen_dataset = next_gen_dataset.merge(
|
||||
df_pivot, how="left", on="uprn"
|
||||
)
|
||||
# We save this dataset, which will be shared with Lewes Council
|
||||
lewes_data.to_csv(
|
||||
"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Newhaven/outputs/Lewes property data.csv", index=False
|
||||
)
|
||||
|
||||
next_gen_dataset.to_csv(
|
||||
"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Newhaven/outputs/next_gen_dataset.csv", index=False
|
||||
)
|
||||
df_pivot = property_scenario_impact.pivot_table(index='uprn', columns='scenario',
|
||||
values=['post_scenario_heating_hotwater_kwh',
|
||||
'post_scenario_heating_hotwater_kwh_scaled'])
|
||||
|
||||
# Flattening multi-index columns
|
||||
df_pivot.columns = [f'{col[0]}_{col[1]}' for col in df_pivot.columns]
|
||||
|
||||
# Reset the index to have a clean dataframe
|
||||
df_pivot.reset_index(inplace=True)
|
||||
|
||||
next_gen_dataset = next_gen_dataset.merge(
|
||||
df_pivot, how="left", on="uprn"
|
||||
)
|
||||
|
||||
next_gen_dataset.to_csv(
|
||||
"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Newhaven/outputs/next_gen_dataset.csv", index=False
|
||||
)
|
||||
|
|
|
|||
78
etl/customers/remote_assessments/app.py
Normal file
78
etl/customers/remote_assessments/app.py
Normal file
|
|
@ -0,0 +1,78 @@
|
|||
import pandas as pd
|
||||
from utils.s3 import save_csv_to_s3
|
||||
|
||||
PORTFOLIO_ID = 111
|
||||
USER_ID = 8
|
||||
|
||||
|
||||
def app():
|
||||
"""
|
||||
This application is used to initialise and run remote assessments
|
||||
:return:
|
||||
"""
|
||||
|
||||
asset_list = [
|
||||
{
|
||||
"uprn": 100050770761,
|
||||
"address": "12 Sheardown Street",
|
||||
"postcode": "DN4 0BH"
|
||||
}
|
||||
]
|
||||
asset_list = pd.DataFrame(asset_list)
|
||||
|
||||
# Store the asset list in s3
|
||||
filename = f"{USER_ID}/{PORTFOLIO_ID}/asset_list.csv"
|
||||
save_csv_to_s3(
|
||||
dataframe=asset_list,
|
||||
bucket_name="retrofit-plan-inputs-dev",
|
||||
file_name=filename
|
||||
)
|
||||
|
||||
non_invasive_recommendations = [
|
||||
{
|
||||
"uprn": 100050770761,
|
||||
"recommendations": [
|
||||
{
|
||||
"type": "extension_cavity_wall_insulation",
|
||||
"sap_points": 2,
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
# Store non-invasive recommendations in S3
|
||||
non_invasive_recommendations_filename = f"{USER_ID}/{PORTFOLIO_ID}/non_invasive_recommendations.csv"
|
||||
save_csv_to_s3(
|
||||
dataframe=pd.DataFrame(non_invasive_recommendations),
|
||||
bucket_name="retrofit-plan-inputs-dev",
|
||||
file_name=non_invasive_recommendations_filename
|
||||
)
|
||||
|
||||
valuation_data = [
|
||||
{
|
||||
"uprn": 100050770761,
|
||||
"value": 67_000
|
||||
}
|
||||
]
|
||||
# Store valuation data to s3
|
||||
valuation_filename = f"{USER_ID}/{PORTFOLIO_ID}/valuation.csv"
|
||||
save_csv_to_s3(
|
||||
dataframe=pd.DataFrame(valuation_data),
|
||||
bucket_name="retrofit-plan-inputs-dev",
|
||||
file_name=valuation_filename
|
||||
)
|
||||
|
||||
body = {
|
||||
"portfolio_id": str(PORTFOLIO_ID),
|
||||
"housing_type": "Private",
|
||||
"goal": "Increasing EPC",
|
||||
"goal_value": "C",
|
||||
"trigger_file_path": filename,
|
||||
"already_installed_file_path": "",
|
||||
"patches_file_path": "",
|
||||
"non_invasive_recommendations_file_path": non_invasive_recommendations_filename,
|
||||
"valuation_file_path": valuation_filename,
|
||||
"scenario_name": "Full package remote assessment",
|
||||
"multi_plan": True,
|
||||
"budget": None,
|
||||
}
|
||||
print(body)
|
||||
|
|
@ -128,6 +128,13 @@ class Recommendations:
|
|||
property_recommendations.append(self.wall_recomender.recommendations)
|
||||
phase += 1
|
||||
|
||||
# We handle recommendations covering specific non-invasive measures
|
||||
new_phase = self.wall_recomender.recommend_extended(phase=phase, measures=measures)
|
||||
if self.wall_recomender.extended_recommendations:
|
||||
property_recommendations.append(self.wall_recomender.extended_recommendations)
|
||||
# We don't have any phasing here
|
||||
phase = new_phase
|
||||
|
||||
self.roof_recommender.recommend(phase=phase, measures=measures, default_u_values=self.default_u_values)
|
||||
if self.roof_recommender.recommendations:
|
||||
property_recommendations.append(self.roof_recommender.recommendations)
|
||||
|
|
@ -470,9 +477,32 @@ class Recommendations:
|
|||
impact_summary = []
|
||||
for recommendations_by_type in property_recommendations:
|
||||
for rec in recommendations_by_type:
|
||||
if rec["type"] in ["mechanical_ventilation", "trickle_vents", "draught_proofing"]:
|
||||
if rec["type"] in [
|
||||
"mechanical_ventilation", "trickle_vents", "draught_proofing", "extension_cavity_wall_insulation"
|
||||
]:
|
||||
# We don't have a percieved sap impact of mechanical ventilation or trickle vents, and we don't
|
||||
# have the capacity to score draught proofing
|
||||
if rec["type"] == "extension_cavity_wall_insulation":
|
||||
|
||||
previous_phase = [x for x in impact_summary if x["phase"] == (rec["phase"] - 1)]
|
||||
if previous_phase:
|
||||
sap = previous_phase[0]["sap"]
|
||||
carbon = previous_phase[0]["carbon"]
|
||||
heat_demand = previous_phase[0]["heat_demand"]
|
||||
else:
|
||||
sap = float(property_instance.data["current-energy-efficiency"])
|
||||
carbon = float(property_instance.data["co2-emissions-current"])
|
||||
heat_demand = float(property_instance.data["energy-consumption-current"])
|
||||
|
||||
impact_summary.append(
|
||||
{
|
||||
"phase": rec["phase"],
|
||||
"recommendation_id": rec["recommendation_id"],
|
||||
"sap": sap + rec["sap_points"],
|
||||
"carbon": carbon - rec["co2_equivalent_savings"],
|
||||
"heat_demand": heat_demand - rec["heat_demand"],
|
||||
}
|
||||
)
|
||||
continue
|
||||
|
||||
phase_energy_efficiency_metrics = {
|
||||
|
|
@ -563,6 +593,17 @@ class Recommendations:
|
|||
property_phase_impact["carbon"], rec["co2_equivalent_savings"]
|
||||
)
|
||||
|
||||
if rec["type"] == "loft_insulation":
|
||||
# When we have a loft insulation recommendation, where there is an extension and the existing
|
||||
# amount of loft insulation is already good, we limit the SAP points
|
||||
# By limiting here, we don't change the value in current_phase_values. This means that the
|
||||
# future recommendations won't have an impact that is too large
|
||||
li_sap_limit = RoofRecommendations.get_loft_insulation_sap_limit(
|
||||
property_instance.data["roof-energy-eff"], property_instance.data["extension-count"]
|
||||
)
|
||||
if li_sap_limit is not None:
|
||||
property_phase_impact["sap"] = min(property_phase_impact["sap"], li_sap_limit)
|
||||
|
||||
# Insert this information into the recommendation.
|
||||
if not rec.get("survey", False):
|
||||
rec["sap_points"] = property_phase_impact["sap"]
|
||||
|
|
@ -664,7 +705,11 @@ class Recommendations:
|
|||
{
|
||||
"phase": r["phase"],
|
||||
"recommendation_id": r["recommendation_id"],
|
||||
"solar_kwh_savings": r["initial_ac_kwh_per_year"] * assumptions.SOLAR_CONSUMPTION_PROPORTION,
|
||||
"solar_kwh_savings": (
|
||||
r["initial_ac_kwh_per_year"] * assumptions.SOLAR_CONSUMPTION_PROPORTION
|
||||
) if not r["has_battery"] else (
|
||||
r["initial_ac_kwh_per_year"] * assumptions.SOLAR_CONSUMPTION_WITH_BATTERY_PROPORTION
|
||||
),
|
||||
} for recs in property_recommendations for r in recs if r["type"] == "solar_pv"
|
||||
], columns=["phase", "recommendation_id", "solar_kwh_savings"])
|
||||
|
||||
|
|
@ -751,7 +796,9 @@ class Recommendations:
|
|||
# We now deduce if any of the recommendations result in a change of fuel type
|
||||
for recs in property_recommendations:
|
||||
for rec in recs:
|
||||
if rec["type"] in ["mechanical_ventilation", "trickle_vents", "draught_proofing"]:
|
||||
if rec["type"] in [
|
||||
"mechanical_ventilation", "trickle_vents", "draught_proofing", "extension_cavity_wall_insulation"
|
||||
]:
|
||||
# We cannot score the impact on draught proofing
|
||||
continue
|
||||
|
||||
|
|
|
|||
|
|
@ -59,6 +59,23 @@ class RoofRecommendations:
|
|||
self.property.roof["is_flat"]
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def get_loft_insulation_sap_limit(cls, roof_energy_eff, extension_count):
|
||||
"""
|
||||
Get the SAP limit for loft insulation
|
||||
:param roof_energy_eff:
|
||||
:return:
|
||||
"""
|
||||
|
||||
if extension_count == 0:
|
||||
# No limit
|
||||
return None
|
||||
|
||||
if roof_energy_eff in ["Good", "Very Good"]:
|
||||
return 1
|
||||
|
||||
return None
|
||||
|
||||
def mds_loft_insulation(self, phase):
|
||||
"""
|
||||
For usages within the mds report
|
||||
|
|
@ -273,7 +290,7 @@ class RoofRecommendations:
|
|||
# loft is already partially insulated.
|
||||
# Note: This requirement is only for loft insulation
|
||||
if (
|
||||
(material["depth"] + insulation_thickness) < self.MINIMUM_RECOMMENDED_LOFT_INSULATION
|
||||
material["depth"] < self.MINIMUM_RECOMMENDED_LOFT_INSULATION
|
||||
) and is_pitched:
|
||||
continue
|
||||
|
||||
|
|
@ -295,6 +312,7 @@ class RoofRecommendations:
|
|||
|
||||
# We allow a small tolerance for error so we don't discount the recommendation entirely
|
||||
if new_u_value <= self.BUILDING_REGULATIONS_PART_L_MAX_U_VALUE:
|
||||
|
||||
lowest_selected_u_value = update_lowest_selected_u_value(lowest_selected_u_value, new_u_value)
|
||||
|
||||
cost_result = self.costs.loft_and_flat_insulation(
|
||||
|
|
|
|||
|
|
@ -196,7 +196,7 @@ class SolarPvRecommendations:
|
|||
]
|
||||
|
||||
roof_area = self.property.roof_area
|
||||
solar_configurations = panel_performance.head(3).reset_index(drop=True)
|
||||
solar_configurations = panel_performance.head(6).reset_index(drop=True)
|
||||
|
||||
# We combine each of these configurations with estimates with and without a battery
|
||||
for rank, recommendation_config in solar_configurations.iterrows():
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
import math
|
||||
from typing import List
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from datatypes.enums import QuantityUnits
|
||||
|
|
@ -99,6 +100,8 @@ class WallRecommendations(Definitions):
|
|||
|
||||
# Will contains a list of recommended measures
|
||||
self.recommendations = []
|
||||
# Contains a list of extended recommendation measures, such as extension insulation
|
||||
self.extended_recommendations = []
|
||||
|
||||
self.cavity_wall_insulation_materials = [
|
||||
part for part in materials if part["type"] == "cavity_wall_insulation"
|
||||
|
|
@ -267,6 +270,91 @@ class WallRecommendations(Definitions):
|
|||
# If the u-value is within regulations, we don't do anything
|
||||
return
|
||||
|
||||
def recommend_extended(self, phase, measures):
|
||||
"""
|
||||
Where we have extended measures, such as extension insulation, which cannot typically be picked up
|
||||
from the EPC api, we handle the recommendation of these here
|
||||
:param measures:
|
||||
:return:
|
||||
"""
|
||||
|
||||
# These are the measures that are covered by this function
|
||||
extended_measures = ["extension_cavity_wall_insulation"]
|
||||
|
||||
measures_to_recommend = [measure for measure in measures if measure in extended_measures]
|
||||
if not measures_to_recommend:
|
||||
return phase
|
||||
|
||||
# We reset this to be empty
|
||||
self.extended_recommendations = []
|
||||
|
||||
recommendation_phase = phase
|
||||
for measure in measures_to_recommend:
|
||||
if measure == "extension_cavity_wall_insulation":
|
||||
recommendation = self.recommend_extension_cavity_wall_insulation(phase=recommendation_phase)
|
||||
else:
|
||||
raise NotImplementedError(f"Measure {measure} is not implemented")
|
||||
recommendation_phase += 1
|
||||
|
||||
self.extended_recommendations.append(recommendation)
|
||||
|
||||
return recommendation_phase
|
||||
|
||||
def recommend_extension_cavity_wall_insulation(self, phase):
|
||||
"""
|
||||
This function produces the recommendation for extension cavity wall insulation
|
||||
:return:
|
||||
"""
|
||||
|
||||
# TODO: We aren't provided with carbon, heat or bill savings figures for this measure
|
||||
|
||||
extension_cavity_insulation_recommendation = [
|
||||
r for r in self.property.non_invasive_recommendations if r["type"] == "extension_cavity_wall_insulation"
|
||||
][0]
|
||||
|
||||
# https://surreybuildingprojects.co.uk/how-much-does-a-24m2-extension-cost
|
||||
average_extension_floor_area = 24
|
||||
# https://assets.publishing.service.gov.uk/media/5f047a01d3bf7f2be8350262
|
||||
# /Size_of_English_Homes_Fact_Sheet_EHS_2018.pdf
|
||||
# This is rough
|
||||
average_house_floor_area = 94
|
||||
|
||||
proposed_extension_floor_area = self.property.floor_area * (
|
||||
average_extension_floor_area / average_house_floor_area
|
||||
)
|
||||
# assume 3 walls are external
|
||||
proposed_extension_insulation_wall_area = (
|
||||
np.sqrt(proposed_extension_floor_area) * self.property.floor_height * 3
|
||||
)
|
||||
|
||||
cost_result = self.costs.cavity_wall_insulation(
|
||||
wall_area=proposed_extension_insulation_wall_area,
|
||||
material=self.cavity_wall_insulation_materials[0],
|
||||
)
|
||||
|
||||
recommendation = {
|
||||
"phase": phase,
|
||||
"parts": [],
|
||||
"type": "extension_cavity_wall_insulation",
|
||||
"measure_type": "extension_cavity_wall_insulation",
|
||||
"description": "Insulate the cavity walls of the extension",
|
||||
"starting_u_value": None,
|
||||
"new_u_value": None,
|
||||
"sap_points": extension_cavity_insulation_recommendation["sap_points"],
|
||||
"heat_demand": 0,
|
||||
"kwh_savings": 0,
|
||||
"energy_savings": 0,
|
||||
"energy_cost_savings": 0,
|
||||
"co2_equivalent_savings": 0,
|
||||
"already_installed": False,
|
||||
"simulation_config": {},
|
||||
"description_simulation": {},
|
||||
**cost_result,
|
||||
"default": True,
|
||||
}
|
||||
|
||||
return recommendation
|
||||
|
||||
def find_cavity_insulation(self, u_value, insulation_thickness, phase, measures, default_u_values):
|
||||
"""
|
||||
This method tests different materials to fill the cavity wall, determining which
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue